Traffic distribution method and device, computer equipment and storage medium

Abstract

The invention belongs to the field of artificial intelligence and the field of digital medical treatment, and relates to a flow distribution method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining account information of a user if an inquiry request is received, and querying the historical information of the user based on the account information; collecting complaint information of the user and acquiring reception information of each doctor; performing factor generation processing on the historical information, the chief complaint information and the reception information to obtain an initial key factor; screening target key factors related to inquiry flow distribution from the initial key factors; generating a target weight corresponding to the target key factor; constructing a multi-flow factor distribution model based on the target key factor and the target weight, and calculating a flow distribution priority value of each doctor based on the multi-flow factor distribution model; and screening out a target doctor with the highest flow allocation priority value from all doctors, and allocating the user to the target doctor. According to the invention, the processing accuracy of inquiry flow distribution is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence development technology and the field of digital medicine, and in particular to a flow distribution method, apparatus, computer equipment and storage medium. Background Art

[0002] With the rapid development of the Internet, the medical and health field has also ushered in a wave of digital transformation. Among them, online medical consultation platforms, as an important bridge connecting patients and doctors, have continued to grow in user scale, and service demands are becoming increasingly diversified and personalized. These changes are not only reflected in patients' urgent needs for instant and efficient medical consultation services, but also in their high standards for service experience, privacy protection and professionalism.

[0003] At present, the traffic allocation mechanism commonly used by online medical consultation platforms is mainly based on the maximum and minimum number of patients preset by the doctor, combined with the doctor's busy and idle status coefficient (such as historical number of patients, response time, etc.), to dynamically adjust the number of concurrent users seen by each doctor. This traffic allocation method cannot accurately identify the user's actual diagnosis and treatment needs for this consultation, resulting in low accuracy in traffic allocation, which in turn affects the user's consultation experience. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a traffic distribution method, device, computer equipment and storage medium to solve the technical problem that the traffic distribution method adopted by the existing medical consultation platform cannot accurately identify the user's actual diagnosis and treatment needs for this consultation, resulting in low accuracy of traffic distribution, thereby affecting the user's consultation experience.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a flow distribution method, which adopts the following technical solution:

[0006] Determine whether a consultation request submitted by a user is received;

[0007] If yes, obtain the user's account information, and query the user's historical information based on the account information;

[0008] Collecting the user's main complaint information and obtaining the consultation information of each doctor;

[0009] Based on a preset factor generation strategy, factor generation processing is performed on the historical information, the chief complaint information, and the patient reception information to obtain corresponding initial key factors;

[0010] Based on a preset effectiveness screening strategy, a target key factor related to the consultation flow distribution is screened out from the initial key factors; wherein the number of the target key factors includes a plurality;

[0011] Generating target weights corresponding to the target key factors;

[0012] Constructing a corresponding multi-flow factor allocation model based on the target key factor and the target weight, and calculating the process allocation priority value of each of the doctors based on the multi-flow factor allocation model;

[0013] A target doctor with the highest process assignment priority value is screened out from all the doctors, and the user is assigned to the target doctor.

[0014] Furthermore, the step of selecting target key factors related to the consultation flow distribution from the initial key factors based on the preset effectiveness screening strategy specifically includes:

[0015] Preprocessing the initial key factor to obtain a corresponding first factor;

[0016] Performing a correlation analysis on the first factor to obtain a correlation coefficient value between the first factor and the quality of the consultation;

[0017] Performing an importance assessment on the first factor based on a preset machine learning model to obtain an importance score of the first factor;

[0018] Screening out second factors that meet preset screening conditions from all the first factors based on the correlation coefficient value and the importance score;

[0019] A target key factor related to the consultation flow distribution is generated based on the second factor.

[0020] Furthermore, the step of generating a target key factor related to the consultation flow distribution based on the second factor specifically includes:

[0021] Selecting a third factor that meets a preset relevance threshold and a preset importance threshold from the second factors;

[0022] Performing redundant factor removal processing on the third factor to obtain a corresponding fourth factor;

[0023] Get the business significance information of all the specified factors that are filtered;

[0024] extracting important factors from the specified factors based on the business significance information;

[0025] Based on the important factor, the fourth factor is supplemented to obtain a corresponding fifth factor;

[0026] The fifth factor is used as the target key factor.

[0027] Furthermore, the step of generating target weights corresponding to the target key factors specifically includes:

[0028] Get the preset weight distribution algorithm;

[0029] Performing weight calculation processing on the target key factor based on a weight allocation algorithm to obtain an initial weight corresponding to the target key factor;

[0030] Receive input weight feedback information;

[0031] Fine-tuning the initial weight of the target key factor based on the weight feedback information to obtain a fine-tuned initial weight;

[0032] The fine-tuned initial weight is used as the target weight of the target key factor.

[0033] Furthermore, the step of constructing a corresponding multi-flow factor allocation model based on the target key factors and the target weights specifically includes:

[0034] Constructing a calculation formula for calculating the traffic allocation priority based on the target key factor and the target weight;

[0035] Generate corresponding target calculation logic based on the calculation formula;

[0036] Model building processing is performed based on the target calculation logic to obtain the multi-flow factor allocation model.

[0037] Furthermore, after the step of selecting a target doctor with the highest process allocation priority value from all the doctors and assigning the user to the target doctor, the method further includes:

[0038] After the user completes the consultation, collecting the user's evaluation data on the consultation;

[0039] Obtaining feedback from the target doctor on the traffic allocation corresponding to the user;

[0040] Performing data analysis on the evaluation data and the feedback opinions to obtain corresponding data analysis results;

[0041] The multi-flow factor allocation model is optimized based on the data analysis results.

[0042] Furthermore, after the step of selecting a target doctor with the highest process allocation priority value from all the doctors and assigning the user to the target doctor, the method further includes:

[0043] Obtaining the target doctor's consultation status and the user's consultation status;

[0044] Updating the consultation status of the target doctor;

[0045] The user's consultation status is updated.

[0046] In order to solve the above technical problems, the embodiment of the present application further provides a flow distribution device, which adopts the following technical solution:

[0047] A judgment module, used to judge whether a consultation request submitted by a user is received;

[0048] A query module, used to obtain the user's account information and query the user's historical information based on the account information;

[0049] The first processing module is used to collect the user's main complaint information and obtain the consultation information of each doctor;

[0050] A first generating module is used to perform factor generating processing on the historical information, the chief complaint information and the patient reception information based on a preset factor generating strategy to obtain corresponding initial key factors;

[0051] A screening module, used to screen out target key factors related to the consultation flow distribution from the initial key factors based on a preset effectiveness screening strategy; wherein the number of the target key factors includes a plurality;

[0052] A second generating module is used to generate a target weight corresponding to the target key factor;

[0053] A second processing module is used to construct a corresponding multi-flow factor allocation model based on the target key factor and the target weight, and calculate the process allocation priority value of each doctor based on the multi-flow factor allocation model;

[0054] The allocation module is used to screen out a target doctor with the highest process allocation priority value from all the doctors, and allocate the user to the target doctor.

[0055] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0056] Determine whether a consultation request submitted by a user is received;

[0057] If yes, obtain the user's account information, and query the user's historical information based on the account information;

[0058] Collecting the user's main complaint information and obtaining the consultation information of each doctor;

[0059] Based on a preset factor generation strategy, factor generation processing is performed on the historical information, the chief complaint information, and the patient reception information to obtain corresponding initial key factors;

[0060] Based on a preset effectiveness screening strategy, a target key factor related to the consultation flow distribution is screened out from the initial key factors; wherein the number of the target key factors includes a plurality;

[0061] Generating target weights corresponding to the target key factors;

[0062] Constructing a corresponding multi-flow factor allocation model based on the target key factor and the target weight, and calculating the process allocation priority value of each of the doctors based on the multi-flow factor allocation model;

[0063] A target doctor with the highest process assignment priority value is screened out from all the doctors, and the user is assigned to the target doctor.

[0064] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0065] Determine whether a consultation request submitted by a user is received;

[0066] If yes, obtain the user's account information, and query the user's historical information based on the account information;

[0067] Collecting the user's main complaint information and obtaining the consultation information of each doctor;

[0068] Based on a preset factor generation strategy, factor generation processing is performed on the historical information, the chief complaint information, and the patient reception information to obtain corresponding initial key factors;

[0069] Based on a preset effectiveness screening strategy, a target key factor related to the consultation flow distribution is screened out from the initial key factors; wherein the number of the target key factors includes a plurality;

[0070] Generating target weights corresponding to the target key factors;

[0071] Constructing a corresponding multi-flow factor allocation model based on the target key factor and the target weight, and calculating the process allocation priority value of each of the doctors based on the multi-flow factor allocation model;

[0072] A target doctor with the highest process assignment priority value is screened out from all the doctors, and the user is assigned to the target doctor.

[0073] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0074] The present application first determines whether a consultation request submitted by a user is received; if so, obtains the user's account information, and queries the user's historical information based on the account information; then collects the user's chief complaint information, and obtains the consultation information of each doctor; then, based on a preset factor generation strategy, the historical information, the chief complaint information and the consultation information are subjected to factor generation processing to obtain corresponding initial key factors; and based on a preset effectiveness screening strategy, target key factors related to consultation flow distribution are screened out from the initial key factors; wherein the number of target key factors includes multiple; subsequently, a target weight corresponding to the target key factor is generated; further, a corresponding multi-flow factor allocation model is constructed based on the target key factor and the target weight, and the multi-flow factor allocation model is used to calculate the effectiveness of each doctor. Process allocation priority value; finally, the target doctor with the highest process allocation priority value is screened out from all the doctors, and the user is assigned to the target doctor. In this way, the present application can accurately determine a number of target key factors that are in line with medical scenarios and related to consultation traffic allocation through the use of factor generation strategy and effectiveness screening strategy, and will build a corresponding multi-flow factor allocation model based on the target weights corresponding to the target key factors, and then calculate the process allocation priority value of each doctor based on the use of the multi-flow factor allocation model, and screen out the target doctor with the highest process allocation priority value from all doctors, and finally assign the user to the target doctor, so as to automatically and accurately complete the consultation traffic allocation processing for the user, effectively improve the processing accuracy of consultation traffic allocation, and thus help improve the user's consultation experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0076] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0077] Figure 2 A flow chart of an embodiment of a flow distribution method according to the present application;

[0078] Figure 3 is a schematic structural diagram of an embodiment of a flow distribution device according to the present application;

[0079] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0080] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0081] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0082] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0083] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0084] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0085] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, the tablet computer 1012 or the mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer, a desktop computer, etc.

[0086] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0087] It should be noted that the traffic distribution method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the traffic distribution device is generally arranged in the server / terminal device.

[0088] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0089] Continue to refer Figure 2 , showing a flow chart of an embodiment of the flow distribution method according to the present application. According to different requirements, the order of the steps in the flow chart can be changed, and some steps can be omitted. The flow distribution method provided in the embodiment of the present application can be applied to any scenario where flow distribution is required, and the flow distribution method can be applied to products in these scenarios, for example, consultation flow distribution in the field of digital medicine. The flow distribution method comprises the following steps:

[0090] Step S201, determining whether a consultation request submitted by a user is received.

[0091] In this embodiment, the electronic device (eg Figure 1The server / terminal device shown in the figure) can obtain the consultation request through a wired connection or a wireless connection. It should be noted that the above-mentioned wireless connection method may include but is not limited to 3G / 4G / 5G connection, Wi-Fi connection, Bluetooth connection, Wi MAX connection, Zigbee connection, UWB (Ultra Wi DeBand) connection, and other wireless connection methods currently known or to be developed in the future. The executor of the present application is specifically a traffic distribution system, or simply referred to as a system. The present application can be applied to the consultation traffic distribution scenario in the field of digital medicine, specifically for assigning suitable doctors to users for corresponding consultation processing. The above-mentioned consultation request may be a consultation request submitted by the user through the traffic distribution system.

[0092] Step S202: If yes, obtain the user's account information, and query the user's historical information based on the account information.

[0093] In this embodiment, during the process of the user logging into the traffic distribution system, the user's account information can be obtained. The account information may refer to the identity information used by the user when registering the identity in the traffic distribution system, such as name or mobile phone number. Then, the account information can be retrieved from the preset user database to obtain the historical information of the user. Among them, the above-mentioned historical information may at least include user portrait information, user historical access information, user transaction information, consultation interval information, recent transaction doctor information, etc. Specifically, the user portrait information may include gender, age, and past history. Historical access information may include visited modules and search keywords. User transaction information may include: number of transactions, transaction amount, name of purchased drug category, and name of purchased physical commodity category. Consultation interval information includes the number of minutes between this consultation and the last consultation. The most recent transaction doctor includes the doctor information of the user's most recent transaction.

[0094] Step S203, collecting the user's chief complaint information and obtaining the consultation information of each doctor.

[0095] In this embodiment, when a user submits a consultation request, the user's chief complaint information is collected through the front-end interface. The chief complaint information refers to the symptom description or question that the user is currently consulting. The above-mentioned consultation information refers to the real-time consultation status of each doctor, which may at least include: the number of patients the doctor receives in real time, the maximum number of patients configured by the doctor, and the number of patients in the doctor's real-time queue. The consultation information of each doctor can be obtained by querying the doctor status table or real-time calculation.

[0096] Step S204: performing factor generation processing on the historical information, the chief complaint information and the consultation information based on a preset factor generation strategy to obtain corresponding initial key factors.

[0097] In this embodiment, the similarity of the main complaint can be obtained by calculating the text similarity coefficient between the user's current main complaint information and the historical main complaint information using the above main complaint information. Specifically, the keywords in the current main complaint information can be obtained, and the text natural language similarity analysis of the historical medical consultation requests can be performed, and the relevance of the current main complaint information and the historical main complaint information can be determined based on the similarity.

[0098] Subsequently, the user portrait information, user historical access information, user transaction information, consultation interval information, recent transaction doctor information, the above-mentioned chief complaint similarity, and the real-time number of patients seen by the doctor, the maximum number of patients configured by the doctor, and the real-time number of patients in the doctor's queue contained in the above-mentioned historical information obtained by calculation or query are integrated to obtain the corresponding initial key factors.

[0099] Step S205, based on a preset effectiveness screening strategy, target key factors related to the consultation flow distribution are screened out from the initial key factors; wherein the number of the target key factors includes a plurality.

[0100] In this embodiment, the specific implementation process of screening out the target key factors related to the consultation flow distribution from the initial key factors based on the preset effectiveness screening strategy will be described in further detail in subsequent specific embodiments of the present application and will not be elaborated on here.

[0101] Step S206: Generate a target weight corresponding to the target key factor.

[0102] In this embodiment, the specific implementation process of generating the target weights corresponding to the target key factors will be further described in detail in subsequent specific embodiments of the present application, and will not be elaborated on here.

[0103] Step S207: construct a corresponding multi-flow factor allocation model based on the target key factors and the target weights, and calculate the process allocation priority value of each doctor based on the multi-flow factor allocation model.

[0104] In this embodiment, the specific implementation process of the above-mentioned multi-flow factor allocation model corresponding to the target key factors and the target weights will be further described in detail in subsequent specific embodiments of the present application and will not be elaborated here.

[0105] Step S208, screening out a target doctor with the highest process assignment priority value from all the doctors, and assigning the user to the target doctor.

[0106] In this embodiment, numerical comparison can be performed on all process allocation priority values ​​to screen out a target doctor with the highest process allocation priority value from all the doctors, and then the user can be assigned to the target doctor to automatically complete the user's consultation traffic allocation processing.

[0107] The present application first determines whether a consultation request submitted by a user is received; if so, obtains the user's account information, and queries the user's historical information based on the account information; then collects the user's chief complaint information, and obtains the consultation information of each doctor; then, based on a preset factor generation strategy, the historical information, the chief complaint information and the consultation information are subjected to factor generation processing to obtain corresponding initial key factors; and based on a preset effectiveness screening strategy, target key factors related to consultation flow distribution are screened out from the initial key factors; wherein the number of target key factors includes multiple; subsequently, a target weight corresponding to the target key factor is generated; further, a corresponding multi-flow factor allocation model is constructed based on the target key factor and the target weight, and the multi-flow factor allocation model is used to calculate the effectiveness of each doctor. Process allocation priority value; finally, the target doctor with the highest process allocation priority value is screened out from all the doctors, and the user is assigned to the target doctor. In this way, the present application can accurately determine a number of target key factors that are in line with medical scenarios and related to consultation traffic allocation through the use of factor generation strategy and effectiveness screening strategy, and will build a corresponding multi-flow factor allocation model based on the target weights corresponding to the target key factors, and then calculate the process allocation priority value of each doctor based on the use of the multi-flow factor allocation model, and screen out the target doctor with the highest process allocation priority value from all doctors, and finally assign the user to the target doctor, so as to automatically and accurately complete the consultation traffic allocation processing for the user, effectively improve the processing accuracy of consultation traffic allocation, and thus help improve the user's consultation experience.

[0108] In some optional implementations, step S205 includes the following steps:

[0109] The initial key factor is preprocessed to obtain a corresponding first factor.

[0110] In this embodiment, the preprocessing refers to performing descriptive statistical analysis on the initial key factors to identify abnormal values ​​or missing values ​​in the initial key factors, and performing appropriate processing, such as filling, deleting or marking.

[0111] A correlation analysis is performed on the first factor to obtain a correlation coefficient value between the first factor and the quality of the medical consultation.

[0112] In this embodiment, the correlation between the first factors can be evaluated by using methods such as the Pearson correlation coefficient and the Spearman rank correlation coefficient, and the correlation between the first factors and the quality of the consultation (such as user satisfaction and doctor evaluation) can be further analyzed to obtain the corresponding absolute value of the correlation coefficient as the above-mentioned correlation coefficient value.

[0113] An importance assessment is performed on the first factor based on a preset machine learning model to obtain an importance score of the first factor.

[0114] In this embodiment, the machine learning model is a pre-built model with the quality of the consultation as the target variable and each first factor as the characteristic variable, and specifically a linear regression model, a logistic regression model or a random forest model can be used. The performance of the model can be evaluated by using methods such as cross-validation to determine which factors contribute the most to the model prediction, and then the characteristic importance of each first factor is calculated to obtain the above importance score.

[0115] Based on the correlation coefficient value and the importance score, a second factor meeting a preset screening condition is screened out from all the first factors.

[0116] In this embodiment, the above-mentioned preset screening condition refers to the condition that the correlation coefficient value is greater than the preset correlation threshold, or the importance score is greater than the preset importance threshold. By screening the first factor based on the preset screening condition, the second factor with a high correlation with the consultation flow distribution can be preliminarily screened out. Among them, there is no specific limitation on the values ​​of the above-mentioned correlation threshold and importance threshold, which can be set according to actual business needs and model performance requirements.

[0117] A target key factor related to the consultation flow distribution is generated based on the second factor.

[0118] In this embodiment, the specific implementation process of generating the target key factor related to the consultation flow distribution based on the second factor will be further described in detail in subsequent specific embodiments of the present application, and will not be elaborated on here.

[0119] The present application obtains the corresponding first factor by preprocessing the initial key factor; then performs a correlation analysis on the first factor to obtain a correlation coefficient value between the first factor and the consultation quality; and performs an importance assessment on the first factor based on a preset machine learning model to obtain an importance score of the first factor; then, based on the correlation coefficient value and the importance score, a second factor that meets the preset screening conditions is screened out from all the first factors; and subsequently, a target key factor related to the consultation traffic distribution is generated based on the second factor. The present application obtains the first factor by preprocessing the initial key factor, and then screens the first factor in combination with the correlation coefficient value and the importance score obtained by performing a correlation analysis and importance assessment on the first factor. This can intelligently and accurately screen out the target key factor related to the consultation traffic distribution from the first factor, thereby ensuring the data accuracy of the obtained target key factor.

[0120] In some optional implementations of this embodiment, generating a target key factor related to consultation flow distribution based on the second factor includes the following steps:

[0121] A third factor that meets a preset relevance threshold and a preset importance threshold is selected from the second factors.

[0122] In this embodiment, there is no specific limitation on the values ​​of the above-mentioned correlation threshold and importance threshold, and they can be set according to actual business needs and model performance requirements.

[0123] Redundant factor removal is performed on the third factor to obtain a corresponding fourth factor.

[0124] In this embodiment, the redundant factor removal process includes checking whether there is a high correlation (such as multicollinearity) between factors. If there are redundant factors, one or more of them are excluded to reduce the complexity of the model.

[0125] Get the business significance information of all the specified factors that are filtered.

[0126] In this embodiment, in addition to correlation statistics and feature importance analysis, the business significance of each factor can be further considered. For example, even if the statistical correlation of a factor is not strong, if it represents important business logic (such as the user's historical disease type), it should be retained. Among them, the above-mentioned specified factors refer to factors that have been filtered out, that is, other factors in the above-mentioned initial key factors except the above-mentioned fourth factor.

[0127] Important factors are extracted from the designated factors based on the business significance information.

[0128] In this embodiment, the above-mentioned important factors refer to factors existing in the specified factors and representing important business logic.

[0129] The fourth factor is supplemented based on the important factor to obtain a corresponding fifth factor.

[0130] In this embodiment, the fourth factor and the important factor are integrated to obtain the corresponding fifth factor.

[0131] The fifth factor is used as the target key factor.

[0132] The present application selects a third factor that meets the preset relevance threshold and the preset importance threshold from the second factor; then removes redundant factors from the third factor to obtain the corresponding fourth factor; then obtains the business significance information of all the specified factors that are filtered; and extracts important factors from the specified factors based on the business significance information; subsequently supplements the fourth factor based on the important factor to obtain the corresponding fifth factor; and finally uses the fifth factor as the target key factor. After the present application selects the second factor that meets the preset screening condition from all the first factors based on the correlation coefficient value and the importance score, it will also intelligently select the third factor that meets the preset relevance threshold and the preset importance threshold from the second factor, and remove redundant factors from the third factor to obtain the fourth factor, and further extract important factors from the specified factor based on the business significance information of the filtered specified factor, and then supplement the fourth factor based on the important factor to obtain the final target key factor, which effectively improves the screening accuracy of the target key factor, ensures the diversity and reliability of the obtained target key factor, and is conducive to ensuring the model effect of the subsequent multi-flow factor allocation model corresponding to the target key factor and the target weight.

[0133] In some optional implementations, step S206 includes the following steps:

[0134] Get the preset weight distribution algorithm.

[0135] In this embodiment, the weight allocation algorithm may specifically adopt an algorithm such as AHP (Analytic Hierarchy Process) or an expert scoring method.

[0136] The target key factors are weighted based on a weight allocation algorithm to obtain initial weights corresponding to the target key factors.

[0137] In this embodiment, the target key factors may be weighted according to the steps of constructing a hierarchy, performing pairwise comparisons, and calculating comprehensive weights corresponding to the selected weight allocation algorithm, thereby obtaining initial weights corresponding to the target key factors.

[0138] Receive input weight feedback information.

[0139] In this embodiment, experts in the medical field may be further invited to participate in the weight allocation process of the above-mentioned target key factors, and receive the experts' experience and suggestions to obtain the above-mentioned weight feedback information.

[0140] The initial weight of the target key factor is fine-tuned based on the weight feedback information to obtain the fine-tuned initial weight.

[0141] In this embodiment, the initial weights of the target key factors may be fine-tuned accordingly based on the experience and suggestions mentioned in the above weight feedback information, so as to obtain the fine-tuned initial weights.

[0142] The fine-tuned initial weight is used as the target weight of the target key factor.

[0143] The present application obtains a preset weight distribution algorithm; and based on the weight distribution algorithm, performs weight calculation processing on the target key factor to obtain the initial weight corresponding to the target key factor; then receives input weight feedback information; and then fine-tunes the initial weight of the target key factor based on the weight feedback information to obtain the fine-tuned initial weight; and subsequently uses the fine-tuned initial weight as the target weight of the target key factor. The present application performs weight calculation processing on the target key factor based on the use of a weight distribution algorithm to obtain the initial weight corresponding to the target key factor, and then intelligently fine-tunes the initial weight of the target key factor based on the input weight feedback information to obtain the final target weight of the target key factor, which effectively ensures the accuracy of the obtained target weight, and is conducive to ensuring the model effect of the subsequent multi-traffic factor allocation model corresponding to the target key factor and the target weight.

[0144] In some optional implementations, the constructing of a corresponding multi-flow factor allocation model based on the target key factor and the target weight in step S207 includes the following steps:

[0145] A calculation formula for calculating the traffic allocation priority is constructed based on the target key factor and the target weight.

[0146] In this embodiment, the calculation formula of the above-mentioned flow distribution priority can specifically adopt weighted summation, product or other complex mathematical expressions. Preferably, the calculation formula of the above-mentioned flow distribution priority includes: flow distribution priority = weight value of the most recent consultation * (1 / number of minutes between the last consultation with the same doctor) + transaction weight value * number of transactions + similar complaint weight * correlation coefficient with the last complaint + number of patients weight value * (real-time number of patients / maximum number of patients) - number of queue weight value * number of people currently in the queue for this doctor.

[0147] Generate corresponding target calculation logic based on the calculation formula.

[0148] In this embodiment, according to the above calculation formula, the corresponding model calculation logic, that is, the above target calculation logic, is implemented by using programming languages ​​such as Python. The implementation process of the target calculation logic includes defining functions or classes to encapsulate the calculation process.

[0149] Model building processing is performed based on the target calculation logic to obtain the multi-flow factor allocation model.

[0150] In this embodiment, the model is constructed by taking the above target calculation logic as the calculation logic of the multi-flow factor allocation model, thereby obtaining the corresponding multi-flow factor allocation model. After the construction of the multi-flow factor allocation model is completed, the multi-flow factor allocation model can be used to calculate the flow allocation priority of the relevant doctors using the calculation formula corresponding to the target calculation logic.

[0151] In addition, after completing the construction of the multi-factor traffic allocation model, you can also write unit tests for each part of the model to ensure that each function or method works as expected. And in the absence of actual data, you can use simulated data to test the various functions of the model. And run the test cases to check if there are any failures or exceptions, and fix the problems found. The tested multi-factor traffic allocation model will be integrated into the consultation process of the medical service platform to ensure that the multi-factor traffic allocation model can correctly allocate doctors to users and the allocation results are in line with expectations.

[0152] The present application constructs a calculation formula for calculating the traffic allocation priority based on the target key factors and the target weights; then generates the corresponding target calculation logic based on the calculation formula; and then performs model construction processing based on the target calculation logic to obtain the multi-traffic factor allocation model. The present application constructs a calculation formula for calculating the traffic allocation priority based on the target key factors and the target weights, and generates the corresponding target calculation logic based on the calculation formula, and then performs model construction processing based on the use of the target calculation logic, so that the required multi-traffic factor allocation model can be constructed quickly and accurately, the construction efficiency of the multi-traffic factor allocation model is improved, and the model effect of the obtained multi-traffic factor allocation model is guaranteed.

[0153] In some optional implementations of this embodiment, after step S208, the electronic device may further perform the following steps:

[0154] After the user completes the consultation, the user's evaluation data on the consultation is collected.

[0155] In this embodiment, after the user completes the consultation, the user's star rating for the consultation, that is, the above-mentioned evaluation data, can be collected through the front-end interface of the system, and the evaluation data can be saved in a database for subsequent analysis.

[0156] Obtain feedback from the target doctor on the traffic allocation corresponding to the user.

[0157] In this embodiment, the doctor's feedback on the flow distribution corresponding to the user's consultation process can be collected through the doctor interface or the system, and the feedback can be saved in a database.

[0158] Perform data analysis on the evaluation data and the feedback to obtain corresponding data analysis results.

[0159] In this embodiment, the user's evaluation data and the target doctor's feedback can be analyzed by using statistical methods or machine learning models to evaluate the actual performance of each target key factor and identify invalid factors or redundant factors in the target key factors.

[0160] The multi-flow factor allocation model is optimized based on the data analysis results.

[0161] In this embodiment, the above-mentioned model optimization process includes: adjusting the weight of each target key factor according to the data analysis results. Eliminating invalid factors and recalculating the weight of the remaining factors. In addition, the calculation formula for calculating the traffic distribution priority in the traffic distribution model can also be adjusted.

[0162] In addition, the above optimization process can be automated to form a regularly executed task or real-time monitoring system. New data is continuously collected, feedback from users and doctors is analyzed, and the multi-factor flow distribution model is continuously optimized. The multi-factor flow distribution model is also regularly evaluated and adjusted to adapt to changes in medical scenarios and changes in user needs.

[0163] This application collects the user's evaluation data on the consultation after the user completes the consultation; then obtains the target doctor's feedback on the traffic allocation corresponding to the user; then performs data analysis on the evaluation data and the feedback to obtain the corresponding data analysis results; and then optimizes the multi-flow factor allocation model based on the data analysis results. This application intelligently collects the user's evaluation data on the consultation after the user completes the consultation, and obtains the target doctor's feedback on the traffic allocation corresponding to the user, and then performs data analysis on the evaluation data and the feedback, and optimizes the multi-flow factor allocation model based on the obtained data analysis results, so as to realize real-time dynamic optimization of the factors and weights in the multi-flow factor allocation model according to the feedback from users and doctors, eliminate invalid factors and redundant factors, and continuously maintain the practicality and effectiveness of the multi-flow factor allocation model, so as to adapt to changes in medical scenarios and changes in user needs.

[0164] In some optional implementations of this embodiment, after step S208, the electronic device may further perform the following steps:

[0165] Obtain the target doctor's consultation status and the user's consultation status.

[0166] In this embodiment, the target doctor's reception status information can be queried to obtain the corresponding target doctor's reception status. And the user's consultation status information can be queried to obtain the corresponding user's consultation status. Among them, the original status corresponding to the target doctor's reception status is not receiving consultation. The user's consultation status is waiting for consultation.

[0167] The reception status of the target doctor is updated.

[0168] In this embodiment, the corresponding update process can be completed by updating the target doctor's seeing status from not seeing a patient to seeing a patient.

[0169] The user's consultation status is updated.

[0170] In this embodiment, the corresponding update process can be completed by updating the user's consultation status from waiting for consultation to being consulted.

[0171] This application obtains the target doctor's reception status and the user's consultation status; then updates the target doctor's reception status; and updates the user's consultation status. After assigning the user to the target doctor, this application will automatically and intelligently update the target doctor's reception status and the user's consultation status, thereby effectively ensuring the accuracy of the target doctor's reception status and the user's consultation status.

[0172] In some optional implementations, the user information obtained is subject to the user's consent and complies with relevant laws and policies.

[0173] In addition, the traffic distribution method proposed in this application can reduce the user's waiting time for consultation and the number of consultation rounds, minimize the user's extra queuing time and the cost of explaining to the doctor, so that the user can obtain more efficient and comfortable medical services. By increasing the binding relationship between users and doctors, bilateral stickiness is enhanced. It also simplifies the doctor's operating procedures, reduces the communication costs between doctors and patients, saves doctors' operating time, and improves doctors' operating experience. It also increases the favorability of new users for the first use of the medical platform, increases the frequency of users' follow-up visits and repurchases, gradually increases the referral rate and user consumption unit price, and effectively improves the user's experience and satisfaction and dependence on the platform. It can also reduce the situation of traffic distribution errors in scenarios where medical traffic is saturated, and reduce the probability of uneven distribution of doctors' busy and idle times.

[0174] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0175] It should be emphasized that in order to further ensure the privacy and security of the above process allocation priority value, the above process allocation priority value can also be stored in a node of a blockchain.

[0176] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0177] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0178] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0179] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0180] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0181] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a flow distribution device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0182] like Figure 3As shown, the flow distribution device 300 described in this embodiment includes: a judgment module 301, a query module 302, a first processing module 303, a first generation module 304, a screening module 305, a second generation module 306, a second processing module 307 and a distribution module 308. Among them:

[0183] The judgment module 301 is used to judge whether a consultation request submitted by a user is received;

[0184] A query module 302 is used to obtain the user's account information and query the user's historical information based on the account information;

[0185] The first processing module 303 is used to collect the user's chief complaint information and obtain the consultation information of each doctor;

[0186] The first generating module 304 is used to perform factor generating processing on the historical information, the chief complaint information and the patient reception information based on a preset factor generating strategy to obtain corresponding initial key factors;

[0187] A screening module 305 is used to screen out target key factors related to the consultation flow distribution from the initial key factors based on a preset effectiveness screening strategy; wherein the number of the target key factors includes a plurality;

[0188] The second generating module 306 is used to generate a target weight corresponding to the target key factor;

[0189] The second processing module 307 is used to construct a corresponding multi-flow factor allocation model based on the target key factor and the target weight, and calculate the process allocation priority value of each doctor based on the multi-flow factor allocation model;

[0190] The allocation module 308 is used to screen out a target doctor with the highest process allocation priority value from all the doctors, and allocate the user to the target doctor.

[0191] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the flow distribution method in the above-mentioned implementation mode, and are not described in detail here.

[0192] In some optional implementations of this embodiment, the screening module 305 includes:

[0193] A preprocessing submodule, used for preprocessing the initial key factor to obtain a corresponding first factor;

[0194] An analysis submodule, used for performing a correlation analysis on the first factor to obtain a correlation coefficient value between the first factor and the quality of the consultation;

[0195] An evaluation submodule, configured to evaluate the importance of the first factor based on a preset machine learning model to obtain an importance score of the first factor;

[0196] A screening submodule, configured to screen out second factors that meet preset screening conditions from all the first factors based on the correlation coefficient value and the importance score;

[0197] The first generating submodule is used to generate a target key factor related to the consultation flow distribution based on the second factor.

[0198] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the flow distribution method in the above-mentioned implementation mode, and are not described in detail here.

[0199] In some optional implementations of this embodiment, the first generation submodule includes:

[0200] A screening unit, used to screen out a third factor that meets a preset relevance threshold and a preset importance threshold from the second factors;

[0201] A processing unit, configured to perform redundant factor removal processing on the third factor to obtain a corresponding fourth factor;

[0202] An acquisition unit, used to acquire business significance information of all filtered specified factors;

[0203] An extraction unit, configured to extract important factors from the specified factors based on business significance information;

[0204] A supplementing unit, configured to perform supplementary processing on the fourth factor based on the important factor to obtain a corresponding fifth factor;

[0205] A determination unit is used to use the fifth factor as the target key factor.

[0206] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the flow distribution method in the above-mentioned implementation mode, and are not described in detail here.

[0207] In some optional implementations of this embodiment, the second generating module 306 includes:

[0208] An acquisition submodule is used to acquire a preset weight distribution algorithm;

[0209] A calculation submodule, used to perform weight calculation processing on the target key factor based on a weight allocation algorithm to obtain an initial weight corresponding to the target key factor;

[0210] A receiving submodule, used for receiving input weight feedback information;

[0211] A fine-tuning submodule, configured to fine-tune the initial weight of the target key factor based on the weight feedback information to obtain the fine-tuned initial weight;

[0212] A determination submodule is used to use the fine-tuned initial weight as the target weight of the target key factor.

[0213] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the flow distribution method in the above-mentioned implementation mode, and are not described in detail here.

[0214] In some optional implementations of this embodiment, the second processing module 307 includes:

[0215] A first construction submodule is used to construct a calculation formula for calculating the flow distribution priority based on the target key factor and the target weight;

[0216] A second generating submodule, used to generate corresponding target calculation logic based on the calculation formula;

[0217] The second construction submodule is used to perform model construction processing based on the target calculation logic to obtain the multi-flow factor allocation model.

[0218] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the flow distribution method in the above-mentioned implementation mode, and are not described in detail here.

[0219] In some optional implementations of this embodiment, the flow distribution device further includes:

[0220] A collection module, used for collecting the user's evaluation data on the consultation after the user completes the consultation;

[0221] A first acquisition module, used for acquiring feedback from the target doctor on the flow allocation corresponding to the user;

[0222] An analysis module, used to perform data analysis on the evaluation data and the feedback opinions to obtain corresponding data analysis results;

[0223] An optimization module is used to perform model optimization processing on the multi-flow factor allocation model based on the data analysis results.

[0224] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the flow distribution method in the above-mentioned implementation mode, and are not described in detail here.

[0225] In some optional implementations of this embodiment, the flow distribution device further includes:

[0226] A second acquisition module is used to acquire the reception status of the target doctor and the consultation status of the user;

[0227] A first updating module, used for updating the reception status of the target doctor;

[0228] The second updating module is used to update the user's consultation status.

[0229] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the flow distribution method in the above-mentioned implementation mode, and are not described in detail here.

[0230] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0231] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital processors (DSPs), embedded devices, etc.

[0232] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0233] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the flow distribution method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0234] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions or process data stored in the memory 41, such as computer-readable instructions for running the flow distribution method.

[0235] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0236] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0237] In an embodiment of the present application, through the use of factor generation strategies and effectiveness screening strategies, several target key factors that are in line with medical scenarios and related to consultation traffic distribution can be accurately determined, and a corresponding multi-traffic factor allocation model will be constructed based on the target weights corresponding to the target key factors. Then, based on the use of the multi-traffic factor allocation model, the process allocation priority value of each doctor is calculated, and the target doctor with the highest process allocation priority value is screened out from all doctors. Finally, the user is assigned to the target doctor to automatically and accurately complete the consultation traffic allocation processing for the user, effectively improving the processing accuracy of the consultation traffic allocation, which is beneficial to improving the user's consultation experience.

[0238] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned traffic distribution method.

[0239] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0240] In an embodiment of the present application, through the use of factor generation strategies and effectiveness screening strategies, several target key factors that are in line with medical scenarios and related to consultation traffic distribution can be accurately determined, and a corresponding multi-traffic factor allocation model will be constructed based on the target weights corresponding to the target key factors. Then, based on the use of the multi-traffic factor allocation model, the process allocation priority value of each doctor is calculated, and the target doctor with the highest process allocation priority value is screened out from all doctors. Finally, the user is assigned to the target doctor to automatically and accurately complete the consultation traffic allocation processing for the user, effectively improving the processing accuracy of the consultation traffic allocation, which is beneficial to improving the user's consultation experience.

[0241] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0242] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A flow distribution method, characterized in that: The steps include: Determine whether a consultation request submitted by a user is received; If yes, obtain the user's account information, and query the user's historical information based on the account information; Collecting the user's main complaint information and obtaining the consultation information of each doctor; Based on a preset factor generation strategy, factor generation processing is performed on the historical information, the chief complaint information, and the patient reception information to obtain corresponding initial key factors; Based on a preset effectiveness screening strategy, a target key factor related to the consultation flow distribution is screened out from the initial key factors; wherein the number of the target key factors includes a plurality; Generating target weights corresponding to the target key factors; Constructing a corresponding multi-flow factor allocation model based on the target key factor and the target weight, and calculating the process allocation priority value of each of the doctors based on the multi-flow factor allocation model; A target doctor with the highest process assignment priority value is screened out from all the doctors, and the user is assigned to the target doctor.

2. The flow distribution method according to claim 1, characterized in that: The step of selecting target key factors related to the consultation flow distribution from the initial key factors based on the preset effectiveness screening strategy specifically includes: Preprocessing the initial key factor to obtain a corresponding first factor; Performing a correlation analysis on the first factor to obtain a correlation coefficient value between the first factor and the quality of the consultation; Performing an importance assessment on the first factor based on a preset machine learning model to obtain an importance score of the first factor; Screening out second factors that meet preset screening conditions from all the first factors based on the correlation coefficient value and the importance score; A target key factor related to the consultation flow distribution is generated based on the second factor.

3. The flow distribution method according to claim 2, characterized in that: The step of generating a target key factor related to the consultation flow distribution based on the second factor specifically includes: Selecting a third factor that meets a preset relevance threshold and a preset importance threshold from the second factors; Performing redundant factor removal processing on the third factor to obtain a corresponding fourth factor; Get the business significance information of all the specified factors that are filtered; extracting important factors from the specified factors based on the business significance information; Based on the important factor, the fourth factor is supplemented to obtain a corresponding fifth factor; The fifth factor is used as the target key factor.

4. The flow distribution method according to claim 1, characterized in that: The step of generating the target weight corresponding to the target key factor specifically includes: Get the preset weight distribution algorithm; Performing weight calculation processing on the target key factor based on a weight allocation algorithm to obtain an initial weight corresponding to the target key factor; Receive input weight feedback information; Fine-tuning the initial weight of the target key factor based on the weight feedback information to obtain a fine-tuned initial weight; The fine-tuned initial weight is used as the target weight of the target key factor.

5. The flow distribution method according to claim 1, characterized in that: The step of constructing a corresponding multi-flow factor allocation model based on the target key factor and the target weight specifically includes: Constructing a calculation formula for calculating the traffic allocation priority based on the target key factor and the target weight; Generate corresponding target calculation logic based on the calculation formula; Model building processing is performed based on the target calculation logic to obtain the multi-flow factor allocation model.

6. The flow distribution method according to claim 1, characterized in that: After the step of selecting a target doctor with the highest process allocation priority value from all the doctors and assigning the user to the target doctor, the method further includes: After the user completes the consultation, collecting the user's evaluation data on the consultation; Obtaining feedback from the target doctor on the traffic allocation corresponding to the user; Performing data analysis on the evaluation data and the feedback opinions to obtain corresponding data analysis results; The multi-flow factor allocation model is optimized based on the data analysis results.

7. The flow distribution method according to claim 1, characterized in that: After the step of selecting a target doctor with the highest process allocation priority value from all the doctors and assigning the user to the target doctor, the method further includes: Obtaining the target doctor's consultation status and the user's consultation status; Updating the consultation status of the target doctor; The user's consultation status is updated.

8. A flow distribution device, characterized in that: include: A judgment module, used to judge whether a consultation request submitted by a user is received; A query module, used to obtain the user's account information and query the user's historical information based on the account information; The first processing module is used to collect the user's main complaint information and obtain the consultation information of each doctor; A first generating module is used to perform factor generating processing on the historical information, the chief complaint information and the consultation information based on a preset factor generating strategy to obtain corresponding initial key factors; A screening module, used to screen out target key factors related to the consultation flow distribution from the initial key factors based on a preset effectiveness screening strategy; wherein the number of the target key factors includes a plurality; A second generating module is used to generate a target weight corresponding to the target key factor; A second processing module is used to construct a corresponding multi-flow factor allocation model based on the target key factor and the target weight, and calculate the process allocation priority value of each doctor based on the multi-flow factor allocation model; The allocation module is used to screen out a target doctor with the highest process allocation priority value from all the doctors, and allocate the user to the target doctor.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the flow distribution method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the flow distribution method according to any one of claims 1 to 7 are implemented.

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